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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Buletin Ilmiah Sarjana Teknik Elektro Indonesian Journal of Electrical Engineering and Computer Science
Teddy Surya Gunawan
International Islamic University Malaysia
Computer Science & IT Electrical & Electronics Engineering Engineering

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On the review of image and video-based depression detection using machine learning Arselan Ashraf; Teddy Surya Gunawan; Bob Subhan Riza; Edy Victor Haryanto; Zuriati Janin
Indonesian Journal of Electrical Engineering and Computer Science Vol 19, No 3: September 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v19.i3.pp1677-1684

Abstract

Machine learning has been introduced in the sphere of the medical field to enhance the accuracy, precision, and analysis of diagnostics while reducing laborious jobs. With the mounting evidence, machine learning has the capability to detect mental distress like depression. Since depression is the most prevalent mental disorder in our society at present, and almost the majority of the population suffers from this issue. Hence there is an extreme need for the depression detection models, which will provide a support system and early detection of depression. This review is based on the image and video-based depression detection model using machine learning techniques. This paper analyses the data acquisition techniques along with their databases. The indicators of depression are also reviewed in this paper. The evaluation of different researches, along with their performance parameters, is summarized. The paper concludes with remarks about the techniques used and the future scope of using the image and video-based depression prediction. 
Efficient Pavement Crack Detection and Classification Using Custom YOLOv7 Model Arselan Ashraf; Ali Sophian; Amir Akramin Shafie; Teddy Surya Gunawan; Norfarah Nadia Ismail; Ali Aryo Bawono
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 1: March 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i1.4362

Abstract

It is crucial to detect and classify pavement cracks as part of maintaining road safety. The inspection process for identifying and classifying cracks manually is tedious, time-consuming, and potentially dangerous for inspectors. As a result, an efficient automated approach for detecting road cracks is essential for this development. Numerous issues, such as variations in intensity, uneven data availability, the inefficacy of traditional approaches, and others, make it challenging to accomplish. This research has been carried out to contribute towards developing an efficient pavement crack detection and classification system. This study uses state of the art deep learning algorithm, customized YOLOv7 model. Data from two sources, RDD2022, a publicly available online dataset, and the second set of data gathered from the roads of Malaysia have been used in this investigation. In order to have balanced data for training, many image preprocessing techniques have been applied to the data, such as augmentations, scaling, blurring, etc. Experimental results demonstrate that the detection accuracy of the YOLOv7 model is significant, 92% on the RDD2022 dataset and 88% on our custom dataset. This study reports the outcomes of experiments conducted on both datasets. RDD2022 achieved a precision of 0.9523 and a recall of 0.9545. On the custom dataset, the resulting values for precision and recall were 0.93 and 0.9158, respectively. The results of this study were compared to those of other recent studies in the same field in order toestablish a benchmark. Results from the proposed system were more encouraging and surpassed the benchmarking ones. 
Exploring RRT and BiRRT Algorithms: A Review and Simulation-Based Comparison for Fixed-Wing UAV Path Planning Gilang Nugraha Putu Pratama; Oktaf Agni Dhewa; Mentari Putri Jati; Indra Hidayatulloh; Teddy Surya Gunawan; Syaiful Ardy Gunawan
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 2 (2026): April
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i2.14511

Abstract

Path planning plays a vital role in ensuring the safe and efficient navigation of fixed-wing unmanned aerial vehicles (UAVs), particularly in cluttered and complex environments. The increasing demand for autonomous UAV operations highlights the need for reliable algorithms capable of generating optimal and collision-free trajectories. This study addresses the challenge by reviewing recent uses of the Rapidly-exploring Random Tree (RRT) algorithm in various robotic platforms and navigation tasks. The research contribution of this paper is a comparative analysis of RRT and BiRRT for fixed-wing UAV path planning, quantifying trade-offs between path length, computation time, and obstacle clearance using a real-world 2D urban map. This addresses a gap in the literature, as few studies have directly compared these algorithms specifically for fixed-wing UAV surveillance missions. The methods involve implementing both RRT and BiRRT in a simulated environment where each algorithm is evaluated over 100 runs to measure performance metrics such as path length, computation time, and obstacle clearance. A realistic urban map is used to test the algorithms under consistent starting and goal positions. The results show that both RRT and BiRRT achieve a 100% success rate in finding collision-free paths. BiRRT consistently generates shorter paths and requires less computation time, making it more suitable for time-sensitive missions. However, RRT produces safer trajectories with greater average clearance from obstacles, which is advantageous in environments with high collision risk. The findings demonstrate a clear trade-off between safety and efficiency. In conclusion, BiRRT is recommended for missions where speed and efficiency are prioritized, while RRT is better suited for operations emphasizing safety and obstacle avoidance.
Machine learning-based pavement crack detection, classification, and characterization: a review Arselan Ashraf; Ali Sophian; Amir Akramin Shafie; Teddy Surya Gunawan; Norfarah Nadia Ismail
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5345

Abstract

The detection, classification, and characterization of pavement cracks are critical for maintaining safe road conditions. However, traditional manual inspection methods are slow, costly, and pose risks to inspectors. To address these issues, this article provides a comprehensive overview of state-of-the-art machine vision and machine learning-based techniques for pavement crack detection, classification, and characterization. The paper explores the process flow of these systems, including both machine learning and traditional methodologies. The paper focuses on popular artificial intelligence (AI) techniques like support vector machines (SVM) and neural networks. It underscores the significance of utilizing image processing methods for feature extraction in order to detect cracks. The paper also discusses significant advancements made through deep learning strategies. The main objectives of this research are to improve efficiency and effectiveness in pavement crack detection, reduce inspection costs, and enhance safety. Additionally, the article presents data gathering approaches, various datasets for developing road crack detection models, and compares different models to demonstrate their advantages and limitations. Finally, the paper identifies open challenges in the field and provides valuable insights for future research and development efforts. Overall, this paper highlights the potential of AI-based techniques to revolutionize pavement maintenance practices and significantly improve road safety.
Co-Authors Ali Aboadla Ali Aryo Bawono Ali Sophian Amart Sulong Ameerul Hakeem Mohd Hairon Amir Akramin Shafie Arselan Ashraf Belal A. Hamida Bob Subhan Riza Dmitry Vedenyapin Edy Victor Haryanto Edy Victor Haryanto, Edy Victor Eliathamby Ambikairajah Elsheikh M.A. Elsheikh Erwin Sulaemen Ezzidin Hassan Aboadla Farah Diyana Abdul Rahman Fathiah Abdul Muin Gilang Nugraha Putu Pratama Hasmah Mansor Indra Hidayatulloh Intan Rahmithul Husna Yaldi Malik Arman Morshidi Mashkuri Bin Yaacob Mentari Putri Jati Mimi Aminah binti Wan Nordin Mira Kartiwi Mira Kartiwi Mohamed H. Habaebi Mohd Azmi Omar Muhammad Fahreza Alghifari Muhammad Hamdan Muhammad Hamdan Hasan Gani Muhammad Kasim Lim Muhammad Kassim Lim Nanang Ismail Noreha Abdul Malik Norfarah Nadia Ismail Norfarah Nadia Ismail Nur Atikah Muhamat Saleh Nurul Fariza Zulkurnain Nurul Shaieda Solihin Oktaf Agni Dhewa Othman O. Khalifa Othman Omran Khalifah Rika Rosnelly Sheroz Khan Syaiful Ardy Gunawan Syed Asif Ahmad Qadri Tayseer Mohammed Hasan Asda Tika Arundina Wan Athereah Yahya Yasmin Mahira Saiful Munir Zuriati Janin